scholarly journals Comparing Airborne Particulate Matter Intake Dose Assessment Models Using Low-Cost Portable Sensor Data

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1406
Author(s):  
Rok Novak ◽  
David Kocman ◽  
Johanna Amalia Robinson ◽  
Tjaša Kanduč ◽  
Dimosthenis Sarigiannis ◽  
...  
Keyword(s):  
Heart Rate ◽  
Particulate Matter ◽  
Low Cost ◽  
Minute Ventilation ◽  
Dose Assessment ◽  
Sensor Data ◽  
Activity Data ◽  
Time Activity ◽  
Activity Monitors ◽  
Coarse Information

Low-cost sensors can be used to improve the temporal and spatial resolution of an individual’s particulate matter (PM) intake dose assessment. In this work, personal activity monitors were used to measure heart rate (proxy for minute ventilation), and low-cost PM sensors were used to measure concentrations of PM. Intake dose was assessed as a product of PM concentration and minute ventilation, using four models with increasing complexity. The two models that use heart rate as a variable had the most consistent results and showed a good response to variations in PM concentrations and heart rate. On the other hand, the two models using generalized population data of minute ventilation expectably yielded more coarse information on the intake dose. Aggregated weekly intake doses did not vary significantly between the models (6–22%). Propagation of uncertainty was assessed for each model, however, differences in their underlying assumptions made them incomparable. The most complex minute ventilation model, with heart rate as a variable, has shown slightly lower uncertainty than the model using fewer variables. Similarly, among the non-heart rate models, the one using real-time activity data has less uncertainty. Minute ventilation models contribute the most to the overall intake dose model uncertainty, followed closely by the low-cost personal activity monitors. The lack of a common methodology to assess the intake dose and quantifying related uncertainties is evident and should be a subject of further research.

scholarly journals Wearables and the Quantified Self: Systematic Benchmarking of Physiological Sensors

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4448 ◽  
Author(s):  
Günther Sagl ◽  
Bernd Resch ◽  
Andreas Petutschnig ◽  
Kalliopi Kyriakou ◽  
Michael Liedlgruber ◽  
...  
Keyword(s):  
Heart Rate ◽  
Galvanic Skin Response ◽  
Low Cost ◽  
Wearable Sensors ◽  
Sensor Data ◽  
Quantified Self ◽  
High Quality ◽  
Laboratory Equipment ◽  
Skin Response ◽  
Physiological Sensors

Wearable sensors are increasingly used in research, as well as for personal and private purposes. A variety of scientific studies are based on physiological measurements from such rather low-cost wearables. That said, how accurate are such measurements compared to measurements from well-calibrated, high-quality laboratory equipment used in psychological and medical research? The answer to this question, undoubtedly impacts the reliability of a study’s results. In this paper, we demonstrate an approach to quantify the accuracy of low-cost wearables in comparison to high-quality laboratory sensors. We therefore developed a benchmark framework for physiological sensors that covers the entire workflow from sensor data acquisition to the computation and interpretation of diverse correlation and similarity metrics. We evaluated this framework based on a study with 18 participants. Each participant was equipped with one high-quality laboratory sensor and two wearables. These three sensors simultaneously measured the physiological parameters such as heart rate and galvanic skin response, while the participant was cycling on an ergometer following a predefined routine. The results of our benchmarking show that cardiovascular parameters (heart rate, inter-beat interval, heart rate variability) yield very high correlations and similarities. Measurement of galvanic skin response, which is a more delicate undertaking, resulted in lower, but still reasonable correlations and similarities. We conclude that the benchmarked wearables provide physiological measurements such as heart rate and inter-beat interval with an accuracy close to that of the professional high-end sensor, but the accuracy varies more for other parameters, such as galvanic skin response.

scholarly journals Evaluation methods for low-cost particulate matter sensors

2021 ◽  
Author(s):  
Jeffrey K. Bean
Keyword(s):  
Quality Assurance ◽  
Particulate Matter ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Prediction Interval ◽  
Low Cost ◽  
Sensor Data ◽  
Rural Site ◽  
Mean Square

Abstract. Understanding and improving the quality of data generated from low-cost sensors is a crucial step in using these sensors to fill gaps in air quality measurement and understanding. This paper shows results from a 10-month long campaign that included side-by-side measurements and comparison between EPA-approved reference instruments and low-cost particulate matter sensors in Bartlesville, Oklahoma. At this rural site in the Midwestern United States the instruments typically encountered only low (under 20 µg/m3) concentrations of particulate matter, however higher concentrations (50–400 µg/m3) were observed on three different days during what were likely agricultural burning events. This study focused on methods for understanding and improving data quality for low-cost particulate matter sensors. The data offered insights on how averaging time, choice of reference instrument, and the observation of higher pollutant concentrations can all impact performance indicators (R2 and root mean square error) for an evaluation. The influence of these factors should be considered when comparing one sensor to another or when determining whether a sensor can produce data that fits a specific need. Though R2 and root mean square error remain the dominant metrics in sensor evaluations, an alternative approach using a prediction interval may offer more consistency between evaluations and a more direct interpretation of sensor data following an evaluation. Ongoing quality assurance for sensor data is needed to ensure data continues to meet expectations. Observations of trends in linear regression parameters and sensor bias were used to analyze calibration and other quality assurance techniques.

scholarly journals Measure particulate matter by yourself: data-quality monitoring in a citizen science project

2019 ◽  
Vol 8 (2) ◽  
pp. 317-328 ◽  
Author(s):  
Aboubakr Benabbas ◽  
Martin Geißelbrecht ◽  
Gabriel Martin Nikol ◽  
Lukas Mahr ◽  
Daniel Nähr ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Data Quality ◽  
Urban Areas ◽  
Low Cost ◽  
Quality Monitoring ◽  
Sensor Data ◽  
And Control ◽  
Future Work ◽  
The Impact

Abstract. The concern about air quality in urban areas and the impact of particulate matter (PM) on public health is turning into a big debate. A good solution to sensitize people to this issue is to involve them in the process of air quality monitoring. This paper presents contributions in the field of PM measurements using low-cost sensors. We show how a low-cost PM sensor can be extended to transfer data not only over Wi-Fi but also over the LoRa protocol. Then, we identify some of the correlations existing in the data through data analysis. Afterwards, we show how semantic technologies can help model and control sensor data quality in an increasing PM sensor network. We finally wrap up with a conclusion and plans for future work.

scholarly journals Evaluation methods for low-cost particulate matter sensors

2021 ◽  
Vol 14 (11) ◽  
pp. 7369-7379
Author(s):  
Jeffrey K. Bean
Keyword(s):  
United States ◽  
Quality Assurance ◽  
Particulate Matter ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Low Cost ◽  
Sensor Data ◽  
Rural Site ◽  
Mean Square

Abstract. Understanding and improving the quality of data generated from low-cost sensors represent a crucial step in using these sensors to fill gaps in air quality measurement and understanding. This paper shows results from a 10-month-long campaign that included side-by-side measurements and comparison between reference instruments approved by the United States Environmental Protection Agency (EPA) and low-cost particulate matter sensors in Bartlesville, Oklahoma. At this rural site in the Midwestern United States the instruments typically encountered only low (under 20 µg m−3) concentrations of particulate matter; however, higher concentrations (50–400 µg m−3) were observed on 3 different days during what were likely agricultural burning events. This study focused on methods for understanding and improving data quality for low-cost particulate matter sensors. The data offered insights on how averaging time, choice of reference instrument, and the observation of higher pollutant concentrations can all impact performance indicators (R2 and root mean square error) for an evaluation. The influence of these factors should be considered when comparing one sensor to another or when determining whether a sensor can produce data that fit a specific need. Though R2 and root mean square error remain the dominant metrics in sensor evaluations, an alternative approach using a prediction interval may offer more consistency between evaluations and a more direct interpretation of sensor data following an evaluation. Ongoing quality assurance for sensor data is needed to ensure that data continue to meet expectations. Observations of trends in linear regression parameters and sensor bias were used to analyze calibration and other quality assurance techniques.

Monitoring of 1-Min Personal Particulate Matter Exposures in Relation to Voice-Recorded Time–Activity Data

2001 ◽  
Vol 87 (3) ◽  
pp. 199-213 ◽  
Author(s):  
P.J.E. Quintana ◽  
J.R. Valenzia ◽  
R.J. Delfino ◽  
L.-J.S. Liu
Keyword(s):  
Particulate Matter ◽  
Activity Data ◽  
Time Activity

scholarly journals Feasibility of Using a Smartwatch to Intensively Monitor Patients With Chronic Obstructive Pulmonary Disease: Prospective Cohort Study (Preprint)

2018 ◽  
Author(s):  
Robert Wu ◽  
Daniyal Liaqat ◽  
Eyal de Lara ◽  
Tatiana Son ◽  
Frank Rudzicz ◽  
...  
Keyword(s):  
Physical Activity ◽  
Chronic Obstructive Pulmonary Disease ◽  
Heart Rate ◽  
Pulmonary Disease ◽  
Predictive Analytics ◽  
Sensor Data ◽  
Chronic Obstructive ◽  
Activity Data ◽  
Obstructive Pulmonary Disease ◽  
Technical Issues

BACKGROUND Acute exacerbations of chronic obstructive pulmonary disease (COPD) are associated with accelerated decline in lung function, diminished quality of life, and higher mortality. Proactively monitoring patients for early signs of an exacerbation and treating them early could prevent these outcomes. The emergence of affordable wearable technology allows for nearly continuous monitoring of heart rate and physical activity as well as recording of audio which can detect features such as coughing. These signals may be able to be used with predictive analytics to detect early exacerbations. Prior to full development, however, it is important to determine the feasibility of using wearable devices such as smartwatches to intensively monitor patients with COPD. OBJECTIVE We conducted a feasibility study to determine if patients with COPD would wear and maintain a smartwatch consistently and whether they would reliably collect and transmit sensor data. METHODS Patients with COPD were recruited from 3 hospitals and were provided with a smartwatch that recorded audio, heart rate, and accelerations. They were asked to wear and charge it daily for 90 days. They were also asked to complete a daily symptom diary. At the end of the study period, participants were asked what would motivate them to regularly use a wearable for monitoring of their COPD. RESULTS Of 28 patients enrolled, 16 participants completed the full 90 days. The average age of participants was 68.5 years, and 36% (10/28) were women. Survey, heart rate, and activity data were available for an average of 64.5, 65.1, and 60.2 days respectively. Technical issues caused heart rate and activity data to be unavailable for approximately 13 and 17 days, respectively. Feedback provided by participants indicated that they wanted to actively engage with the smartwatch and receive feedback about their activity, heart rate, and how to better manage their COPD. CONCLUSIONS Some patients with COPD will wear and maintain smartwatches that passively monitor audio, heart rate, and physical activity, and wearables were able to reliably capture near-continuous patient data. Further work is necessary to increase acceptability and improve the patient experience.

scholarly journals Experiences With Wearable Activity Data During Self-Care by Chronic Heart Patients: Qualitative Study (Preprint)

2019 ◽  
Author(s):  
Tariq Osman Andersen ◽  
Henriette Langstrup ◽  
Stine Lomborg
Keyword(s):  
Heart Rate ◽  
Heart Disease ◽  
Chronic Illness ◽  
Self Care ◽  
Clinical Context ◽  
Activity Data ◽  
Time Activity ◽  
Activity Trackers ◽  
Qualitative Interview Study ◽  
Disease Experience

BACKGROUND Most commercial activity trackers are developed as consumer devices and not as clinical devices. The aim is to monitor and motivate sport activities, healthy living, and similar wellness purposes, and the devices are not designed to support care management in a clinical context. There are great expectations for using wearable sensor devices in health care settings, and the separate realms of wellness tracking and disease self-monitoring are increasingly becoming blurred. However, patients’ experiences with activity tracking technologies designed for use outside the clinical context have received little academic attention. OBJECTIVE This study aimed to contribute to understanding how patients with a chronic disease experience activity data from consumer self-tracking devices related to self-care and their chronic illness. Our research question was: “How do patients with heart disease experience activity data in relation to self-care and chronic illness?” METHODS We conducted a qualitative interview study with patients with chronic heart disease (n=27) who had an implanted cardioverter-defibrillator. Patients were invited to wear a FitBit Alta HR wearable activity tracker for 3-12 months and provide their perspectives on their experiences with step, sleep, and heart rate data. The average age was 57.2 years (25 men and 2 women), and patients used the tracker for 4-49 weeks (mean 26.1 weeks). Semistructured interviews (n=66) were conducted with patients 2–3 times and were analyzed iteratively in workshops using thematic analysis and abductive reasoning logic. RESULTS Of the 27 patients, 18 related the heart rate, sleep, and step count data directly to their heart disease. Wearable activity trackers actualized patients’ experiences across 3 dimensions with a spectrum of contrasting experiences: (1) knowing, which spanned gaining insight and evoking doubts; (2) feeling, which spanned being reassured and becoming anxious; and (3) evaluating, which spanned promoting improvements and exposing failure. CONCLUSIONS Patients’ experiences could reside more on one end of the spectrum, could reside across all 3 dimensions, or could combine contrasting positions and even move across the spectrum over time. Activity data from wearable devices may be a resource for self-care; however, the data may simultaneously constrain and create uncertainty, fear, and anxiety. By showing how patients experience self-tracking data across dimensions of knowing, feeling, and evaluating, we point toward the richness and complexity of these data experiences in the context of chronic illness and self-care.

scholarly journals Autonomous Detection System for Non-Hard-Hat Use at Construction Sites Using Sensor Technology

2021 ◽  
Vol 13 (3) ◽  
pp. 1102
Author(s):  
Jung Hoon Kim ◽  
Byung Wan Jo ◽  
Jun Ho Jo ◽  
Yun Sung Lee ◽  
Do Keun Kim
Keyword(s):  
Heart Rate ◽  
Detection System ◽  
Low Cost ◽  
Sensor Data ◽  
Sensor Technology ◽  
Construction Sites ◽  
Novel Method ◽  
Validation Tests ◽  
Microcontroller Unit ◽  
Hard Hat

In this study, we present a novel method of detecting hard hat use on construction sites using a modified version of an off-the-shelf wearable device. The data-transmitting node of the device contained two sensors, a photoplethysmogram (PPG) and accelerometers (Acc), along with two modules, a global positioning system (GPS) and a low-power wide-area (LoRa) network module. All the components were embedded into a microcontroller unit (MCU) in addition to the power supply. The receiving node included a server that displayed the results via both the Internet of Things (IoT) and smartphones. The LoRa network connected two nodes so that it could function in larger areas such as construction sites at a relatively low cost. The proposed method analyzes the data from a PPG sensor located on the hard hat chin strap and automatically notifies a manager when a worker is not wearing the required hard hat at the site. In addition, by utilizing the PPG sensor data, a heart rate abnormality-detecting feature was added based on an age-adjusted maximum heart rate formula. In validation tests, various PPG sensor locations and shapes were studied, and the results demonstrated the smallest error in the circular shaped sensor located at the upper neck (0.56%). Finally, an IoT monitoring page was created to monitor heart rate abnormalities while identifying hard hat use violations via both PCs and smart phones.

scholarly journals Can We Trust Inertial and Heart Rate Sensor Data from an APPLE Watch Device?

Proceedings ◽  
2020 ◽  
Vol 49 (1) ◽  
pp. 128
Author(s):  
Hugo G. Espinosa ◽  
David V. Thiel ◽  
Matthew Sorell ◽  
David Rowlands
Keyword(s):  
Heart Rate ◽  
Inertial Sensor ◽  
Pearson Correlation ◽  
Smart Phone ◽  
Human Movement ◽  
Stair Climbing ◽  
Sensor Data ◽  
Activity Monitors ◽  
Step Counting ◽  
Rate Sensor

The use of wearable technologies for the monitoring of human movement has increased considerably in the past few years, with applications to sports and other physical activities. Energy expenditure, walking and running distance, step count, and heart rate are some of the metrics provided by such devices via smart phone applications. Most of the research studies have involved validating the accuracy and reliability of the activity monitors by using the post-processed data from the device. The aim of this preliminary study was to determine if we can trust sensor data obtained from an Apple watch. This study evaluated the pre-processed data from the watch through step counting and heart rate measurements, and compared it with known validated devices (in-house 9DOF inertial sensor and Polar H10TM). Repeated activities (walking, jogging, and stair climbing) of varying duration and intensity were conducted by participants of varying age and body mass index (BMI). Pearson correlation (r > 0.95) and Bland–Altman statistical analyses were applied to the data to determine the level of agreement between the validated devices and the watch. The sensors from the Apple watch counted steps and measured heart rate with a minimum error and performed as expected.

scholarly journals Experiences With Wearable Activity Data During Self-Care by Chronic Heart Patients: Qualitative Study

10.2196/15873 ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. e15873
Author(s):  
Tariq Osman Andersen ◽  
Henriette Langstrup ◽  
Stine Lomborg
Keyword(s):  
Heart Rate ◽  
Heart Disease ◽  
Chronic Illness ◽  
Self Care ◽  
Clinical Context ◽  
Activity Data ◽  
Time Activity ◽  
Activity Trackers ◽  
Qualitative Interview Study ◽  
Disease Experience

Background Most commercial activity trackers are developed as consumer devices and not as clinical devices. The aim is to monitor and motivate sport activities, healthy living, and similar wellness purposes, and the devices are not designed to support care management in a clinical context. There are great expectations for using wearable sensor devices in health care settings, and the separate realms of wellness tracking and disease self-monitoring are increasingly becoming blurred. However, patients’ experiences with activity tracking technologies designed for use outside the clinical context have received little academic attention. Objective This study aimed to contribute to understanding how patients with a chronic disease experience activity data from consumer self-tracking devices related to self-care and their chronic illness. Our research question was: “How do patients with heart disease experience activity data in relation to self-care and chronic illness?” Methods We conducted a qualitative interview study with patients with chronic heart disease (n=27) who had an implanted cardioverter-defibrillator. Patients were invited to wear a FitBit Alta HR wearable activity tracker for 3-12 months and provide their perspectives on their experiences with step, sleep, and heart rate data. The average age was 57.2 years (25 men and 2 women), and patients used the tracker for 4-49 weeks (mean 26.1 weeks). Semistructured interviews (n=66) were conducted with patients 2–3 times and were analyzed iteratively in workshops using thematic analysis and abductive reasoning logic. Results Of the 27 patients, 18 related the heart rate, sleep, and step count data directly to their heart disease. Wearable activity trackers actualized patients’ experiences across 3 dimensions with a spectrum of contrasting experiences: (1) knowing, which spanned gaining insight and evoking doubts; (2) feeling, which spanned being reassured and becoming anxious; and (3) evaluating, which spanned promoting improvements and exposing failure. Conclusions Patients’ experiences could reside more on one end of the spectrum, could reside across all 3 dimensions, or could combine contrasting positions and even move across the spectrum over time. Activity data from wearable devices may be a resource for self-care; however, the data may simultaneously constrain and create uncertainty, fear, and anxiety. By showing how patients experience self-tracking data across dimensions of knowing, feeling, and evaluating, we point toward the richness and complexity of these data experiences in the context of chronic illness and self-care.

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